Text messages using SMS, WHATSAPP forwards, and messaging applications provided by TWITTER and FACEBOOK are sometimes used to cascade messages through the network downstream. Sometimes, these repeated forwards result in loops, the original sender receiving the message through the network.
This capability of repeated forwards down the network permits the spreading of unwanted messages, referred to as spam, and for dissemination of information of uncertain or doubtful truth (e.g., rumors). Corrective actions may be initiated if this symptom is detected early. The challenge is to apply rules for detection of like-messages with the same/similar content and for identifying the proliferation of messages flowing through the network.
Spam detection through clustering is known. Current methods typically operate on a snapshot of data to build clusters for analysis. This method is optimal if there is a burst of messages from one or more senders in an interval.
However, the spread of unwanted messages often occurs over a period of time and sampling for shorter intervals may not always result in observable clusters. This requires a dynamic method where the data for a specific interval is merged with the clustered data from the previous time frame(s) to form revised clusters such that configured monitors analyze these clusters for alerts/actions.